What is acquisition footprint noise in seismic data?

Acquisition footprint is a noise field that appears on 3D seismic amplitude slices or horizons as an interwoven linear crosshatching parallel to the source line and receiver line directions. It is for the most part an expression of inadequate acquisition geometry, resulting in insufficient sampling of the seismic wave field (aliasing) and irregularities in the offset and azimuth distribution, particularly in the cross line direction.

This noise can interfere with the mapping of stratigraphic features and fault patterns, posing a challenge to seismic interpreters working in both exploration and development settings.

To demonstrate the relevance of the phenomenon I show below a gallery of examples from the literature of severe footprint in land data: an amplitude time slice (Figure 1a) and a vertical section (Figure 1b) from a Saudi Arabian case study, some seismic attributes (Figures 2, 3, 4, and 5), and also some modeled streamer data (Figure 6).

Figure 6. Acquisition footprint in the form of low fold striation due to dip streamer acquisition. From Long et al. Copyrighted material.

In my next post I will review (with more examples form literature) some strategies available to either prevent or minimize the footprint with better acquisition parameters and modeling of the stack response; I will also discuss some ways the footprint can be attenuated after the acquisition of the data (with bin regularization/interpolation, dip-steered median filters, and kx ky filters, from simple low-pass to more sophisticated ones) when the above mentioned strategies are not available, due to time/cost constraint or because the interpreter is working with legacy data.

In subsequent posts I will illustrate a workflow to model synthetic acquisition footprint using Python, and how to automatically remove it in the Fourier domain with frequency filters, and then how to remove it from real data.

Thanks for the feedback Pablo. What I would say is that I have seen several cases of this noise making it all the way to the result of ant tracking, or fault likelihood, either generated on an interpreter’s workstation or, more concerning, delivered by service providers. I am not too sure if they end up ‘resolved’ as structural elements but since those volumes are often the ones where faults are automatically (or semiautomatically) picked, it will certainly be a tedious task to separate the genuine faults to go into a 3D geo-model form the spurious ones. I can see a number of approaches to deal with this, in revers order of desirability: 1 – do the tedious work; 2 – run the ant tracking with a directional reject filter, but this will possibly remove also small scale faults of the same orientation; 3 – filter out the footprint from the discontinuity or curvature volume to be the input for the ant tracking, but unless one uses good dip-steered, edge preserving structural smoothing, this will have some effects on the result too (depending on the amount of smoothing); 4 – remove the footprint with well designed, signal preserving frequency filters in the Fourier domain; 5 – better sample the seismic wave field. What do you think?

Usually I see this effect diminish within the first 300-500 ms or so of land data (depending on the geometry), so your first examples (figure 1) is really severe! I presume these cross-hatched patterns are directly proportional to the effective fold? I mean if you showed the fold (after the top and bottom mutes) would it look stripey in cross-section like your figure 1?

I am totally with you Evan on the fact that the effect is often less visible at larger times, partly as a result of less irregular fold. But not always: so I added the two-way-times to all the figures, to show that in same of these cases the effect is still quite strong at large time values. Also, even when it seems to be less pronounced, it is likely still present (I think part of the reason, in addition to better fold, is the natural increase of wavelength with depth, so the stripes are more “spread-over”) and if still there it is likely to affect significantly the edge detection ad curvature – type attributes. I am hoping to show this with a bit of work on the open access data in future posts.

I have seen the same, acquisition noise usually diminishes with increasing time. I have however seen some shallow water 3D that happened to cross a “canyon” (it was still over the platform) where locally water depth doubled. The noise went all the the way to 3s (water was only 25-50m). In this case, it looks as if PSDM was the more appropriate choice, instead of PSTM that was used. AFAIK, the area was sterile.
Another effect I have seen reported by one of our top processing guys was due to tides+ water velocity mismatch on deep water (around 800-1200m). Swaths going one direction showed higher seabed than those going the other way. The difference was up to 5-6ms. This was very important at the time due the need for increased vertical resolution.

Blogroll

Meta

Go ahead if you want to use my code, modify it, improve it, for non-commercial AND for commercial use. You are also welcome to download and reuse my media files - unless otherwise stated. With both code and images, please give full and clear credit to Matteo Niccoli as the author and mycarta.wordpress.com as the source.
WordPress bloggers are welcome to reblog my posts. For republishing outside of WordPress or any other request, please e-mail me at: matteo@mycarta.ca